Surgery Scheduling in a Crisis.
Ontology highlight
ABSTRACT:
SUBMITTER: Argenziano M
PROVIDER: S-EPMC7262972 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
Publications
Surgery Scheduling in a Crisis.
Argenziano Michael M Fischkoff Katherine K Smith Craig R CR
The New England journal of medicine 20200522 23
Similar Datasets
Project description:SummaryCOVID-19 puts health care providers at risk for infection with SARS-CoV-2. Personal protective equipment (PPE) can reduce viral transmission if used properly. We used simulation of an intraoperative crisis involving an infectious outbreak to assess PPE adherence and confidence in PPE use. Simulation of an intraoperative crisis with a patient with COVID-19 revealed gaps in PPE adherence; however, simulation training successfully increased confidence in PPE use and received positive feedback.
| S-EPMC8900741 | biostudies-literature
Project description:BackgroundCoronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has created a global pandemic. Surgical care has been impacted, with concerns raised around surgical safety, especially in terms of laparoscopic versus open surgery. Due to potential aerosol transmission of SARS-CoV-2, precautions during aerosol-generating procedures and production of surgical plume are paramount for the safety of surgical teams.MethodsA rapid review methodology was used with evidence sourced from PubMed, Departments of Health, surgical colleges and other health authorities. From this, a working group of expert surgeons developed recommendations for surgical safety in the current environment.ResultsPre-operative testing of surgical patients with reverse transcription-polymerase chain reaction does not guarantee lack of infectivity due to a demonstrated false-negative rate of up to 30%. All bodily tissues and fluids should therefore be treated as a potential source of COVID-19 infection during operative management. Caution must be taken, especially when using an energy source that produces surgical plumes, and an appropriate capture device should also be used. Limiting the use of such devices or using lower energy devices is desirable. To reduce perceived risks association with desufflation of pneumoperitoneum during laparoscopic surgery, an appropriate suction irrigator system, attached to a high-efficiency particulate air filter, should be used. Additionally, appropriate use of personal protective equipment by the surgical team is necessary during high-risk aerosol-generating procedures.ConclusionsAs a result of the rapid review, evidence-based guidance has been produced to support safe surgical practice.
| S-EPMC7361254 | biostudies-literature
Project description: Not available
| S-EPMC10082423 | biostudies-literature
Project description:BackgroundHealth care is facing many challenges. The recent pandemic has caused a global reflection on how clinical and organizational processes should be organized, which requires the optimization of decision-making among managers and health care professionals to deliver care that is increasingly patient-centered. The efficiency of surgical scheduling is particularly critical, as it affects waiting list management and is susceptible to suboptimal decisions due to its complexity and constraints.ObjectiveIn this study, in collaboration with one of the leading hospitals in Portugal, Centro Hospitalar e Universitário de Santo António (CHUdSA), a heuristic approach is proposed to optimize the management of the surgical center.MethodsCHUdSA's surgical scheduling process was analyzed over a specific period. By testing an optimization approach, the research team was able to prove the potential of artificial intelligence (AI)-based heuristic models in minimizing scheduling penalties-the financial costs incurred by procedures that were not scheduled on time.ResultsThe application of this approach demonstrated potential for significant improvements in scheduling efficiency. Notably, the implementation of the hill climbing (HC) and simulated annealing (SA) algorithms stood out in this implementation and minimized the scheduling penalty, scheduling 96.7% (415/429) and 84.4% (362/429) of surgeries, respectively. For the HC algorithm, the penalty score was 0 in the urology, obesity, and pediatric plastic surgery medical specialties. For the SA algorithm, the penalty score was 5100 in urology, 1240 in obesity, and 30 in pediatric plastic surgery. Together, this highlighted the ability of AI-heuristics to optimize the efficiency of this process and allowed for the scheduling of surgeries at closer dates compared to the manual method used by hospital professionals.ConclusionsIntegrating these solutions into the surgical scheduling process increases efficiency and reduces costs. The practical implications are significant. By implementing these AI-driven strategies, hospitals can minimize patient wait times, maximize resource use, and enhance surgical outcomes through improved planning. This development highlights how AI algorithms can effectively adapt to changing health care environments, having a transformative impact.
| S-EPMC11840878 | biostudies-literature
Project description:When scheduling surgeries in the operating theater, not only the resources within the operating theater have to be considered but also those in downstream units, e.g., the intensive care unit and regular bed wards of each medical specialty. We present an extension to the master surgery schedule, where the capacity for surgeries on ICU patients is controlled by introducing downstream-dependent block types - one for both ICU and ward patients and one where surgeries on ICU patients must not be performed. The goal is to provide better control over post-surgery patient flows through the hospital while preserving each medical specialty's autonomy over its operational surgery scheduling. We propose a mixed-integer program to determine the allocation of the new block types within either a given or a new master surgery schedule to minimize the maximum workload in downstream units. Using a simulation model supported by seven years of data from the University Hospital Augsburg, we show that the maximum workload in the intensive care unit can be reduced by up to 11.22% with our approach while maintaining the existing master surgery schedule. We also show that our approach can achieve up to 79.85% of the maximum workload reduction in the intensive care unit that would result from a fully centralized approach. We analyze various hospital setting instances to show the generalizability of our results. Furthermore, we provide insights and data analysis from the implementation of a quota system at the University Hospital Augsburg.
| S-EPMC9165286 | biostudies-literature
Project description:Efficient scheduling of shared takeaway containers plays a significant role in the sharing economy system. An effective scheduling system ensures the maximization of container reuse, reducing resource waste and environmental pollution. To explore the applicability of different scheduling models for shared takeaway containers, this paper constructs differential game models for three modes: centralized scheduling, decentralized scheduling, and demand-based scheduling. The equilibrium outcomes are compared and analyzed. The research findings indicate that when the revenue from scheduling takeaway containers is low, decentralized scheduling can yield the maximum benefit for takeaway platforms; conversely, when the revenue is high, centralized scheduling offers the greatest benefit. For restaurant enterprises, when the revenue from scheduling is low, if the cost of scheduling is also low, demand-based scheduling can provide the maximum benefit; however, if the cost is high, decentralized scheduling is more advantageous; otherwise, centralized scheduling can maximize the benefits for restaurant enterprises.
| S-EPMC11878947 | biostudies-literature
Project description:Amid the epidemic outbreaks such as COVID-19, a large number of patients occupy inpatient and intensive care unit (ICU) beds, thereby making the availability of beds uncertain and scarce. Thus, elective surgery scheduling not only needs to deal with the uncertainty of the surgery duration and length of stay in the ward, but also the uncertainty in demand for ICU and inpatient beds. We model this surgery scheduling problem with uncertainty and propose an effective algorithm that minimizes the operating room overtime cost, bed shortage cost, and patient waiting cost. Our model is developed using fuzzy sets whereas the proposed algorithm is based on the differential evolution algorithm and heuristic rules. We set up experiments based on data and expert experience respectively. A comparison between the fuzzy model and the crisp (non-fuzzy) model proves the usefulness of the fuzzy model when the data is not sufficient or available. We further compare the proposed model and algorithm with several extant models and algorithms, and demonstrate the computational efficacy, robustness, and adaptability of the proposed framework.
| S-EPMC9742073 | biostudies-literature
Project description:ObjectiveFacilitate the multi-appointment scheduling problems (MASPs) characteristic of longitudinal clinical research studies. Additional goals include: reducing management time, optimizing clinical resources, and securing personally identifiable information.Materials and methodsFollowing a model view controller architecture, we developed a web-based tool written in Python 3.ResultsSmart Scheduling (SMASCH) system facilitates clinical research and integrated care programs in Luxembourg, providing features to better manage MASPs and speed up management tasks. It is available both as a Linux package and Docker image (https://smasch.pages.uni.lu).DiscussionThe long-term requirements of longitudinal clinical research studies justify the employment of flexible and well-maintained frameworks and libraries through an iterative software life-cycle suited to respond to rapidly changing scenarios.ConclusionsSMASCH is a free and open-source scheduling system for clinical studies able to satisfy recent data regulations providing features for better data accountability. Better scheduling systems can help optimize several metrics that ultimately affect the success of clinical studies.
| S-EPMC9150077 | biostudies-literature
Project description:ImportanceOwing to lack of adequate emergency care infrastructure and decline in general surgery workforce, the United States faces a crisis in access to emergency general surgery (EGS) care. Acute care surgery (ACS), an organized system of trauma, general surgery, and critical care, is a proposed solution; however, ACS diffusion remains poorly understood.ObjectiveTo investigate geographic diffusion of ACS models of care and characterize the communities in which ACS implementation is lagging.Design, setting, and participantsA national survey on EGS practices was developed, tested, and administered at all 2811 US acute care hospitals providing EGS to adults between August 2015 and October 2015. Surgeons responsible for EGS coverage at these hospitals were approached. If these surgeons failed to respond to the initial survey implementation, secondary surgeons or chief medical officers at hospitals with only 1 general surgeon were approached.InterventionsSurvey responses on ACS implementation were linked with geocoded hospital data and national census data to determine geographic diffusion of and access to ACS.Main outcomes and measuresWe measured the distribution of hospitals with ACS models of care vs those without over time (diffusion) and by US counties characterized by sociodemographic characteristics of county residents (access).ResultsSurvey response rate was 60% (n = 1690); 272 responding hospitals had implemented ACS by 2015, steadily increasing from 34 in 2001 to 125 in 2010. Acute care surgery implementation has not been uniform. Rural regions have limited ACS access, with hospitals in counties with greater than the 75th percentile population having 5.4 times higher odds (95% CI, 1.66-7.35) of implementing ACS than hospitals in counties with less than 25th percentile population. Communities with greater percentages of adults without a college degree also have limited ACS access (OR, 3.43; 95% CI, 1.81-6.48). However, incorporating EGS into ACS models may be a potential equalizer for poor, black, and Hispanic communities.Conclusions and relevanceUnderstanding and addressing gaps in ACS implementation across communities will be crucial to ensuring health equity for US residents experiencing general surgery emergencies.
| S-EPMC5838713 | biostudies-literature
Project description:Myasthenic crisis is a complication of myasthenia gravis characterized by worsening of muscle weakness, resulting in respiratory failure that requires intubation and mechanical ventilation. Advances in critical care have improved the mortality rate associated with myasthenic crisis. This article reviews the epidemiology of myasthenic crisis and discusses patient evaluation. Therapeutic options including mechanical ventilation and pharmacological and surgical treatments are also discussed.
| S-EPMC3726100 | biostudies-other